NCT05900167

Feeding Intolerance Risk Prediction Model in Patients With Enteral Nutrition Through Nasogastric Tube

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 442 (estimated)
• Sponsor: Xiao Jie Chen · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

To explore the risk factors of enteral feeding intolerance in critically ill patients, build a risk prediction model and verify it, in order to provide reference for early identification and screening of high-risk groups

Detailed description

Based on the previous literature study, the risk factors of enteral feeding intolerance in critically ill patients were obtained, and the general demographic, disease and treatment information of patients were collected. Four machine learning algorithms, namely traditional logistic regression, random forest, support vector machine and naive Bayes, were used to construct risk prediction models, and the optimal model was selected and verified by comparing the model performance

Conditions

• Enteral Nutrition

Interventions

Type	Name	Description
BEHAVIORAL	enteral nutrition	The nutritional status of the patient is assessed by the physician to determine the need for enteral nutrition

Timeline

• Start date: 2023-06-01
• Primary completion: 2024-10-01
• Completion: 2024-10-01
• First posted: 2023-06-12
• Last updated: 2023-06-12